Constraint-Based Recommendation for Software Project Effort Estimation
Bernhard Peischl1, Markus Zanker2,3,
Mihai Nica1, and
Wolfgang Schmid2
1. Technische Universität Graz, Austria
2. Universität Klagenfurt, Austria
3. ConfigWorks Informationssysteme & Consulting GmbH, Austria
2. Universität Klagenfurt, Austria
3. ConfigWorks Informationssysteme & Consulting GmbH, Austria
Abstract—Identifying the most appropriate effort estimation methods is an important aspect for software project management. Within the scope of an software industry cluster project an expert system recommending estimation methods that best match the software development project’s characteristics and context has been developed. The knowledgebased recommender exploits an explicit knowledge base in order to infer matching items based on the software project’s context. The contribution of this article lies in presenting a constraint-based reasoning mechanism for computing recommendable items from a large set of choices and in its application to the domain of software project management. It discusses a recommendation model for effort estimation methods and presents specific extensions like explanation and repair mechanisms that proved exceptionally useful in this application domain. The application was conceptualized and developed in an iterative process and results from two rounds of evaluation are reported.
Index Terms—constraint-based recommendation, software project management, recommender applications
Cite: Bernhard Peischl, Markus Zanker, Mihai Nica, and Wolfgang Schmid, "Constraint-Based Recommendation for Software Project Effort Estimation," Journal of Emerging Technologies in Web Intelligence, Vol. 2, No. 4, pp. 282-290, November 2010. doi:10.4304/jetwi.2.4.282-290
Index Terms—constraint-based recommendation, software project management, recommender applications
Cite: Bernhard Peischl, Markus Zanker, Mihai Nica, and Wolfgang Schmid, "Constraint-Based Recommendation for Software Project Effort Estimation," Journal of Emerging Technologies in Web Intelligence, Vol. 2, No. 4, pp. 282-290, November 2010. doi:10.4304/jetwi.2.4.282-290
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